Staff Product Data Scientist
At Super, data is central to every major decision. As a Staff Product Data Scientist, you will work at the intersection of product and strategy, taking ownership of decision-making, problem framing, and hypothesis generation. Your contributions will directly influence how millions of customers engage with our products. This role is for a data scientist who excels at tackling complex problems, thrives in ambiguity, and enjoys transforming ambiguous questions into clear, strategy-driving decisions. You will be expected to not only report on past events but also to uncover the underlying reasons through causal inference, predict future trends, and produce decision memos that guide strategic actions. You will collaborate closely with Analytics Engineers, who are responsible for building and maintaining production data products. Your role involves defining what needs to be measured and why, ensuring that the data infrastructure is scalable and trustworthy.
- Run deep-dive analyses into customer behaviour, product usage, and commercial performance.
- Go beyond surface-level metrics to uncover causal drivers behind phenomena using rigorous statistical methods.
- Distinguish correlation from cause-and-effect.
- Influence product roadmaps, commercial tactics, and marketing strategies by framing the right questions.
- Design and analyse experiments, including A/B tests and quasi-experiments.
- Deliver decision memos that change the course of decisions, with quantified impact ranges and clear “so what” guidance for VP and C-suite stakeholders.
- Design KPI trees and define metric intent.
- Specify requirements for Analytics Engineers to implement certified, production-grade data products.
- Define tracking plans for instrumentation.
- Specify dashboard requirements and acceptance criteria.
- Ensure data capture supports decision-making needs.
- Collaborate with Product Managers, Commercial leaders, Analytics Engineers, and Marketers.
- Lead problem-framing sessions.
- Challenge assumptions and reframe vague requests into testable hypotheses.
- Bring analytical rigour to high-stakes discussions at the Director and VP levels.
- Bring structure to chaos by translating broad business challenges into sharp analytical problems with measurable outcomes.
- Advanced statistical methods & causal inference: A/B testing, confidence intervals, regression, quasi-experimental designs (difference-in-differences, synthetic controls, regression discontinuity), and an understanding of CUPED and variance reduction techniques
- SQL proficiency for exploratory analysis and data validation (we use Snowflake). Enough to spec requirements for Analytics Engineers.
- Optimisation expertise is not required at this level.
- A track record of producing decision memos, strategy recommendations, or business cases that measurably influenced product and commercial strategy.
- Executive Communication: Excel at transforming complex causal analyses into clear, compelling narratives. Distil findings into “so what” recommendations for VP and C-suite audiences.
- Commercial & Product Acumen: Understand how data connects to business outcomes, product design, and customer behaviour.
- Proactive Problem Framing: Reframe vague asks, propose alternative hypotheses, and drive analytical roadmaps based on business priorities. Comfortable with ambiguity and bias toward “what should we do?” over “what happened?”.
- Minimum 5+ years of relevant experience in analytics, data science, or product data science roles (ideally in product or commercial domains) where you’ve directly influenced strategy and growth through causal analysis and experimentation.
- Degree in Data Science, Statistics, Economics, Computer Science/Engineering, Mathematics, or a related quantitative field, or equivalent work experience.
